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BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

Published by National Renewable Energy Laboratory | Department of Energy | Catalog Last Checked: May 07, 2026 at 08:25 PM | Dataset Last Updated: October 07, 2024 at 03:12 PM
The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

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Complete Metadata
@type dcat:Dataset
accessLevel public
bureauCode 
                                            [
  "019:20"
]
contactPoint 
                                            {
  "fn": "Charles Tripp",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:charles.tripp@nlr.gov"
}
dataQuality 
                                            true
description The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.
distribution 
                                            [
  {
    "@type": "dcat:Distribution",
    "title": "BUTTER-E Paper",
    "format": "08151v1",
    "accessURL": "https://arxiv.org/html/2403.08151v1#S3",
    "mediaType": "application/octet-stream",
    "description": "Paper detailing the BUTTER-E project and dataset."
  },
  {
    "@type": "dcat:Distribution",
    "title": "BUTTER - Empirical Deep Learning Dataset on OEDI",
    "format": "HTML",
    "accessURL": "https://data.openei.org/submissions/5708",
    "mediaType": "text/html",
    "description": "Link to the OEDI submission for the BUTTER dataset which includes a link to the original BUTTER data on AWS, data descriptions, and a tutorial Jupyter notebook for using the data."
  },
  {
    "@type": "dcat:Distribution",
    "title": "BUTTER-E GitHub ReadMe",
    "format": "md",
    "accessURL": "https://github.com/NREL/BUTTER-E-Empirical-analysis-of-energy-trends-in-neural-networks-supplementary-code/blob/main/Readme%20for%20Data.md",
    "mediaType": "application/octet-stream",
    "description": "README document describing the columns, schema, size, and format of the data contained in this submission."
  },
  {
    "@type": "dcat:Distribution",
    "title": "BUTTER-E Energy.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "1-minute raw time series power data corresponding to the runs in the "BUTTER-E Metadata" resource.",
    "downloadURL": "https://data.openei.org/files/5991/butter_e_energy.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Node Info.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "description": "Characteristics of each compute node used to generate the BUTTER-E data set.",
    "downloadURL": "https://data.openei.org/files/5991/node_sinfo.csv"
  },
  {
    "@type": "dcat:Distribution",
    "title": "BUTTER-E Metadata.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "Metadata concerning each training run",
    "downloadURL": "https://data.openei.org/files/5991/butter_e_metadata.csv.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Node Power Distribution.csv",
    "format": "csv",
    "mediaType": "text/csv",
    "description": "Power consumption quantiles for each node used to generate the BUTTER-E Dataset.",
    "downloadURL": "https://data.openei.org/files/5991/node_power_dist.csv"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Runs with Standardized Energy.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "Power data joined to run data, including extra columns for standardized energy data as described in the paper.",
    "downloadURL": "https://data.openei.org/files/5991/runs_with_standardized_energy.csv.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Summary by Epoch.tar",
    "format": "tar",
    "mediaType": "application/octet-stream",
    "description": "Training losses related to the BUTTER-E dataset, re-summarized from the BUTTER dataset.",
    "downloadURL": "https://data.openei.org/files/5991/summary_by_epoch.tar"
  }
]
DOI 10.25984/2329316
identifier https://data.openei.org/submissions/5991
issued 2022-12-30T07:00:00Z
keyword 
                                            [
  "BUTTER",
  "BUTTER-E",
  "benchmark",
  "computational science",
  "deep learning",
  "efficient",
  "empirical deep learning",
  "empirical machine learning",
  "energy",
  "energy consumption",
  "energy efficiency",
  "energy use",
  "green computing",
  "machine learning",
  "model",
  "network structure",
  "neural networks",
  "node-level",
  "power",
  "power consumption",
  "training",
  "training efficiency"
]
landingPage https://data.openei.org/submissions/5991
license https://creativecommons.org/licenses/by/4.0/
modified 2024-10-07T15:12:02Z
programCode 
                                            [
  "019:023"
]
projectNumber GO0028308
projectTitle National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)
publisher 
                                            {
  "name": "National Renewable Energy Laboratory",
  "@type": "org:Organization"
}
spatial 
                                            "{"type":"Polygon","coordinates":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}"
title BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset

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